Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

• URL: http://arxiv.org/abs/2009.10019v4
• Date: Mon, 23 Nov 2020 19:03:06 GMT
• Title: Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion
• Authors: Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg
• Abstract summary: We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
• Score: 95.1825179206694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-level controller that utilizes an established control method to robustly execute the primitives. Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85~percent more energy efficient and is more robust compared to baseline methods. We also deploy the controller on a physical robot without any randomization or adaptation scheme.

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